Warm up

• Check out this article on various visualizations that can be made with Plottly express: Link

  • What did you find the most interesting and relevant? How do you plan to use interactive visualizations in your work?

Module completion checklist

Introduction to Heatmaps

• Exploratory data analysis comes in many forms, but if we are trying to create a model to predict a certain variable then we want to know if our target variable is correlated to our predictors
• This is where a correlation plot comes in handy, each square represents the correlation value between two variables

Creating heatmaps

cormat = iris_dataset.corr()
# Create a heatmap of iris_dataset correlation
fig = px.imshow(cormat, 
          zmin = -1, 
          zmax = 1)
fig.show()

Density heatmaps

• A density heatmap is a two dimensional histogram
• This allows us to see if two variables have approximately the same distribution shape
• This can be very helpful for regression models, if two variables have the same distribution shape, we do not need to do any data transformation for one to predict the other

Density heatmaps (cont’d)

• We can also make a density heatmap between predictors to see if another predictor is going to add to the variance explained

# Create a density heatmap of tips dataset using plotly function density_heatmap
fig = px.density_heatmap(tips_dataset, 
          x = "total_bill", 
          y = "tip")
fig.show()

World map

# Take a subset from gapminder dataset
gapminder_subset = px.data.gapminder().query("year==2007")

# Create a map using plotly function chopleth
fig = px.choropleth(gapminder_subset, 
              locations="iso_alpha",
              color = "lifeExp",  #a variable of gapminder
              hover_name = "country",  # add hover information
              color_continuous_scale = px.colors.sequential.Plasma)
fig.show()

World map (cont’d)

# Take a subset from gapminder dataset
gapminder_subset = px.data.gapminder().query("year==2007")

# Create a map using plotly function chopleth
fig = px.choropleth(gapminder_subset, 
              locations="iso_alpha",
              color = "lifeExp",  #a variable of gapminder
              hover_name = "country",  # add hover information
              color_continuous_scale = px.colors.sequential.Plasma)
fig.show()

The US map

fig = px.choropleth(locations=["CA", "TX", "NY"], 
          locationmode="USA-states", 
          color=[1,2,3], 
          scope="usa")
fig.show()

Downloading a custom GeoJSON to make a map

• Not all data fits nicely on a world map or a U.S. map; perhaps we want to show things at a more granular level, like county or district or even neighborhood level

• If this is the case, we might need to get a ‘geoJSON’ file to add to our map

• A ‘geoJSON’ contains all the border lines for the areas we want our map to be divided into

• You can generally find a ‘geoJSON’ already available for what you are looking to map, but if not, there are plenty of generators that are free online

Downloading a custom GeoJSON to make a map (cont’d)

• We will create a map of Montreal Election data by district
• We need to be sure our locations parameter is set to the location column in our dataframe and the featureidkey parameter is set to the feature key in the JSON which holds the matching granularity

Downloading a custom GeoJSON to make a map (cont’d)

election_dataset = px.data.election()

geojson = px.data.election_geojson()

fig = px.choropleth(election_dataset, 
          geojson=geojson,color="Bergeron",
          locations="district",
          featureidkey="properties.district",
          projection="mercator")
          
fig.update_geos(fitbounds="locations", 
          visible=False)
          
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

fig.show()

Module completion checklist

Animation of a scatter map

gapminder_dataset = px.data.gapminder() 
fig = px.scatter_geo(gapminder_dataset, locations="iso_alpha", 
          color="continent", 
          hover_name="country",
          size="pop",
          animation_frame="year", 
          projection="natural earth")
fig.show()

Animation of a heat map

• We can also add this animation to our choropleth maps using that same argument
• We can add to the animation effects by using the argument animation_group

fig = px.choropleth(gapminder_dataset, 
          locations="iso_alpha",         
          color="lifeExp", 
          hover_name="country",     
          animation_frame="year", 
          range_color=[20,80])          
fig.show()

Knowledge check

Exercise

Module completion checklist

Plotly Express: Topic summary

In this part of the course, we have covered:

• Introduction to plotly express
• Organize and visualize data with plotly express

Congratulations on completing this module!